Current events
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Revision as of 08:02, 27 October 2020
SIAG/FME virtual seminars series
The series of virtual talks, started by the SIAM Activity Group on Financial Mathematics and Engineering (SIAG/FME), aims at keeping the mathematical finance community connected worldwide beyond traditional formats. The goal is to host a diverse, across all dimensions, lineup of prominent speakers that will present the latest developments in the area of financial mathematics and engineering.
\diamond The talks will run every other week, and at least until the next SIAG/FME Biennial Meeting in June 2021
\diamond The talks will alternate with those set up by the Bachelier Finance Society
\diamond All talks will be delivered remotely using Zoom.
\diamond The talks are open to the public. Due to security reasons, all attendees have to register.
\diamond The registration link will be posted on this web-site, next to the each seminar date below. The detailed information about each talk, and the registration link will be also distributed via SIAG/FME Mailing List.
\diamond The registration is quick (asks only for your name and email), and once registered, you will receive an email with the link to the meeting(s), which is unique to you, so please do not share that email. The registration is usually valid for multiple future talks.
SIAG/FME Seminar Series Committee:
\quad Agostino Capponi (SIAG/FME Chair, Columbia University)
\quad Igor Cialenco (SIAG/FME Program Director, Illinois Institute of Technology)
\quad Sebastian Jaimungal (University of Toronto)
\quad Ronnie Sircar (Princeton University)
Forthcoming Talks
Thursday, September 17, 2020, 1PM-2PM (Eastern US; GMT-4); Registration Link
Speaker: Rene Carmona, Princeton University
Title: Contract theory and mean field games to inform epidemic models
Abstract: After a short introduction to contract theory, we review recent results on models involving one principal and a field of agents, both for continuous and discrete state spaces. We conclude with the discussion of an application to the control of the spread of an epidemic to illustrate the potential to inform regulatory decisions.
Moderator: Sebastian Jaimungal, University of Toronto
Thursday, October 1, 2020, 1PM-2PM (Eastern US; GMT-4);
Speaker: Samuel Drapeau, Shanghai Jiao Tong University
Title: TBA
Abstract: TBA
Moderator: Mike Ludkovski, Department of Statistics and Applied Probability, UC Santa Barbara
Thursday, October 15, 2020, 1PM-2PM (Eastern US; GMT-4);
Panel Discussion: Implications of COVID-19 on financial markets
Title: TBA
Abstract: TBA
Moderator: Agostino Capponi, Department of Industrial Engineering and Operations Research, Columbia University
Thursday, October 29, 2020, 1PM-2PM (Eastern US; GMT-4);
Speaker: Francesca Biagini, University of Munich
Title: TBA
Abstract: TBA
Moderator: Jean-Pierre Fouque, Department of Statistics and Applied Probability, UC Santa Barbara
Thursday, November 12, 2020, 1PM-2PM (Eastern US; GMT-4);
Speaker: Damir Filipovic, EPFL
Title: TBA
Abstract: TBA
Moderator:
Thursday, November 26, 2020
Due to Thanksgiving Day, the is no seminar
Thursday, December 10, 2020, 1PM-2PM (Eastern US; GMT-4);
Speaker: Early Career Talks
Title: TBA
Abstract: TBA
Moderator:
.
Past Talks
Thursday, September 3, 2020, 1PM-2PM (Eastern US; GMT-4);
Speaker: Sergey Nadtochiy, Illinois Institute of Technology
Title: A simple microstructural explanation of the concavity of price impact
Abstract: I will present a simple model of market microstructure which explains the concavity of price impact. In the proposed model, the local relationship between the order flow and the fundamental price (i.e. the local price impact) is linear, with a constant slope, which makes the model dynamically consistent. Nevertheless, the expected impact on midprice from a large sequence of co-directional trades is nonlinear and asymptotically concave. The main practical conclusion of the model is that, throughout a meta-order, the volumes at the best bid and ask prices change (on average) in favor of the executor. This conclusion, in turn, relies on two more concrete predictions of the model, one of which can be tested using publicly available market data and does not require the (difficult to obtain) information about meta-orders. I will present the theoretical results and will support them with the empirical analysis.
Moderator: Ronnie Sircar, Princeton University
Thursday, August 20, 2020, 1PM-2PM (Eastern US; GMT-4);
Speaker: Paolo Guasoni, Dublin City University
Title: The cost of Lightning Network channels and its implications for the network's structure Recorded video
Abstract: A channel in the Lightning Network is a protocol to secure bitcoin payments and escrow holdings between two parties, designed to increase transaction immediacy and reduce blockchain congestion. In a lightning channel, each party commits collateral towards future payments to the counterparty. Payments are cryptographically secured updates of the collaterals. This paper obtains conditions under which two parties optimally establish a channel, finds explicit formulas for channels’ costs, and derives implications for the network’s structure under cooperation assumptions among small sets of users. As optimal network structures eschew redundant channels, they typically exhibit low degree. If agents’ payment rates are sufficiently homogeneous, centralization through a common intermediary may become optimal.
Moderator: Agostino Capponi, Columbia University
Thursday, July 23, 2020, 1PM-2PM (Eastern US; GMT-4);
Early Career Talks
Ruimeng Hu, University of California Santa Barbara
Title: Deep fictitious play for stochastic differential games Recorded Video
Abstract: Differential games, as an offspring of game theory and optimal control, provide the modeling and analysis of conflict in the context of a dynamic system. Computing Nash equilibria is one of the core objectives in differential games, with a major bottleneck coming from the notorious intractability of N-player games. This leads to the difficulty of the curse of dimensionality, which will be overcome by the algorithms of deep fictitious play using machine learning tools. We discuss the approaches to solve open-loop and Markovian Nash equilibria with convergence analysis.
A. Max Reppen, Boston University
Title: Discrete dividend payments in continuous time Recorded Video
Abstract: We propose a model in which dividend payments occur at regular, deterministic intervals in an otherwise continuous model. This contrasts traditional models where either the payment of continuous dividends is controlled or the dynamics are given by discrete time processes. Moreover, between two dividend payments, the structure allows for other types of control; we consider the possibility of equity issuance at any point in time. The value is characterized as the fixed point of an optimal control problem with periodic initial and terminal conditions. We prove the regularity and uniqueness of the corresponding dynamic programming equation, and the convergence of an efficient numerical algorithm that we use to study the problem. The model enables us to find the loss caused by infrequent dividend payments. We show that under realistic parameter values this loss varies from around 1% to 24% depending on the state of the system, and that using the optimal policy from the continuous problem further increases the loss.
Moderator: Igor Cialenco, Illinois Institute of Technology
Thursday, June 25, 2020, 1PM-2PM (Eastern US; GMT-4);
Speaker: Jean-Pierre Fouque, University of California Santa Barbara
Title: Accuracy of Approximation for Portfolio Optimization under Multiscale Stochastic Environment Recorded Video
Abstract: For the problem of portfolio optimization when returns and volatilities are driven by stochastic factors, approximations for value functions and optimal strategies have been proposed in the regime where these factors are running on slow and fast timescales. But, until now, rigorous results of accuracy of these approximations have only been obtained for cases that can be linearized, typically limited to power utilities and a single factor driving the environment. This talk is about treating cases with general utility functions and multi factors. Our approach is to construct sub- and super- solutions to the fully nonlinear problem such that their difference is at the desired level of accuracy. We first present a regular perturbation case with a power utility and two factors nearly fully correlated. Then, we show how to deal with a singular perturbation in the case of a general utility function with a fast varying factor.
Joint work with Maxim Bichuch, Ruimeng Hu, and Ronnie Sircar.
Moderator: Agostino Capponi, Department of Industrial Engineering and Operations Research, Columbia University
Thursday, June 11, 2020, 1PM-2PM (Eastern US; GMT-4);
Speaker: Patrick Cheridito, ETH Zurich
Title: Deep optimal stopping Recorded Video
Abstract: I present a deep learning method for optimal stopping problems which directly learns the optimal stopping rule from Monte Carlo samples. As such, it is broadly applicable in situations where the underlying randomness can efficiently be simulated. The approach is tested on three problems: the pricing of a Bermudan max-call option, the pricing of a callable multi barrier reverse convertible and the problem of optimally stopping a fractional Brownian motion. In all three cases it produces very accurate results in high-dimensional situations with short computing times. Joint work with Sebastian Becker and Arnulf Jentzen.
Moderator: Sebastian Jaimungal, Department of Statistical Sciences, University of Toronto
Thursday, May 28, 2020, 1PM-2PM (Eastern US; GMT-4);
Panel Discussion: Energy Markets
Abstract: The aim is to discuss recent events in energy/electricity/commodity markets, such as negative prices, as well as related mathematical modeling challenges.
Panelists:
\qquad Rene Aid, Université Paris-Dauphine, France
\qquad Glen Swindle, Scoville Risk Partners, USA
\qquad Zef Lokhandwalla, Bloomberg LP, USA
\qquad Mike Ludkovski, University of California Santa Barbara, USA
Moderator: Ronnie Sircar, ORFE, Princeton University
Thursday, May 14, 2020, 1PM-2:30PM (Eastern US; GMT-4);
Speaker: Bruno Dupire, Head of Quantitative Research, Bloomberg LP
Title: The Geometry of Money and the Perils of Parameterization
Abstract: Market participants use parametric forms to make sure prices are orderly aligned. It may prevent static arbitrages but could it lead to dynamic arbitrages? Markets trade thousands of underlying, each one with tens or even hundreds of options, quoted throughout the day. Needless to say, the quotes are not generated manually. They are automated and derived from a functional form with a few parameters. If we know this parameterization, we know in advance how the prices tomorrow of many traded securities will belong to a low dimensional (number of parameters) manifold in a high dimensional (number of securities). If the vector of today prices does not belong to the convex hull of the manifold it creates arbitrage. We examine market practice (Black-Scholes, stochastic volatility models, interest rate interpolation by piecewise constant instantaneous forward rates, converging implied volatilities for extreme strikes in FX...) and show that many violate the no arbitrage condition.
Moderator: Igor Cialenco, Illinois Institute of Technology
Thursday, April 30, 2020, 1PM-2PM (Eastern US; GMT-4);
Speaker: Blanka Horvath, Department of Mathematics, King's College London, UK
Title: A Data-driven Market Simulator for Small Data Environments
Abstract: In this talk we investigate how Deep Hedging brings a new impetus into the modelling of financial markets. While a DNN-based data-driven market generation unveils a new and highly flexible way of modelling financial time series, it is by no means "model-free". In fact, the concrete modelling choice is decisive for the features of the resulting generative model. After a very short walk through historical market models we proceed to neural network based generative modelling approaches for financial time series. We then investigate some of the challenges to achieve good results in the latter, and highlight some applications and pitfalls. While most generative models tend to rely on large amounts of training data, we present here a parsimonious generative model that works reliably even in environments where the amount of available training data is notoriously small. Furthermore, we discuss how a rough paths perspective combined with a parsimonious Variational Autoencoder framework provides a powerful way for encoding and evaluating financial time series data in such environments. Lastly, we also discuss some pricing and hedging considerations in a DNN framework and their connection to Market Generation. The talk is based on joint work with H. Buehler, I. Perez Arribaz, T. Lyons and B. Wood.
Moderator: Agostino Capponi, Department of Industrial Engineering and Operations Research, Columbia University
Thursday, April 16, 2020, 1PM-2PM (Eastern US; GMT-4)
Speaker: Mete Soner, Department of Operations Research and Financial Engineering, Princeton University
Title: Trading with impact
Abstract: It is well known that large trades cause unfavorable price impact resulting in trading losses. These losses are particularly high when the underlying instrument is not liquid enough or when the trade size is large. Other type of market frictions such as transaction costs also cause similar effects. When one considers hedging or portfolio management or equilibrium models these effects must be taken into account. After describing widely used approaches of Cetin, Jarrow & Protter and Almgren & Chris, I first study the impact of resilience and then the structure of the optimal portfolios. This talk will be a summary of many results obtained jointly with many people including, Peter Bank, Bruno Bouchard, Umut Cetin, Ludovic Moreau, Johannes Muhle-Karbe, Nizar Touzi and Moritz Voss.
Moderator: Sebastian Jaimungal, Department of Statistical Sciences, University of Toronto
